Insurance fraud is a growing concern for insurance companies. Fraud has become pervasive and is not strictly limited to the insured. In fact, in the health care industry a growing number of health care providers have been actively and systematically participating in insurance fraud schemes. As a result, insurance rates have skyrocketed and have showed no signs of abatement.
One problem with insurance fraud detection is that insurance investigators may be employed or may be active in different industries (e.g., healthcare, car insurance, home insurance, etc.) and/or may be employed by different insurance companies within the same industry. As a result, a fraud scheme which may span different industries or which may be employed by an individual known to one company but not known to another company may go undetected. This is so, because the data stores associated with industries and companies are often in different formats from one another, such that even if the information is shared between multiple data stores, it may still take an unreasonable amount of time to manually assimilate the information between the different data stores. Thus, a perpetrator of the fraud may be long gone before the situation is ever detected by an investigator.
In fact, data store disparity may and often does exist within the same insurance company, such as when a large insurance company has a plurality of divisions or regions and each division or region has its own unique data store collection and storage system. This is particularly true with a growing number of insurance companies that have merged with one another in recent years. During organizational mergers, the integration of data collection and storage is a long and continuous process or is often an exercise that is abandoned altogether due to expense. In situations like this, companies often attempt to integrated data through periodic extractions and manual analysis between their disparate data sources. However, this is an untimely exercise and is often much too late to catch a fraud perpetrator.
Problems with managing fraud data are compounded when a particular data store adds new fields, modifies label or field names, and/or removes existing fields. When this occurs, any other data store which attempts to manually and periodically integrate with the modified data store must also be adjusted to proper account for the changes. These types of modifications also alter reports. Furthermore, some reports are used for purposes of litigation and often require that they be reproduced in previous or older formats. As a result, integration is never truly achieved and if integration is achieved it is achieved at an enormous expense and is frequently untimely.
In still other situations, one fraud investigator may deploy a particular set of fraud patterns which is different from another fraud investigator's fraud patterns. Consequently, when the two fraud investigators attempt to share patterns and data sources, their integration is a difficult and timely exercise.
In yet other situations, data sources which are capable of being shared are generally not shared because of privacy and security concerns and because of the inability of one or more parties to properly integrate and ensure privacy or security to the satisfaction of the other party.
Therefore, there is a need for improved techniques managing fraud information.